PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

“…</p><p dir="ltr">In terms of classification, a second algorithm was developed and employed to preliminary sort or group the individual cells (after excluding the overlapping cells manually) into different categories using five geometric measurements applied to the extracted contour from each binary image mask (see PathOlOgics_script_2; preliminary shape measurements). …”

Image1_Identification of Six Prognostic Genes in EGFR–Mutant Lung Adenocarcinoma Using Structure Network Algorithms.JPEG by Haomin Zhang (4427188)

Table3_Identification of Six Prognostic Genes in EGFR–Mutant Lung Adenocarcinoma Using Structure Network Algorithms.XLSX by Haomin Zhang (4427188)

Table2_Identification of Six Prognostic Genes in EGFR–Mutant Lung Adenocarcinoma Using Structure Network Algorithms.XLSX by Haomin Zhang (4427188)

Image2_Identification of Six Prognostic Genes in EGFR–Mutant Lung Adenocarcinoma Using Structure Network Algorithms.JPEG by Haomin Zhang (4427188)

Table1_Identification of Six Prognostic Genes in EGFR–Mutant Lung Adenocarcinoma Using Structure Network Algorithms.XLSX by Haomin Zhang (4427188)

Image_2_Using Bayesian networks with Max-Min Hill-Climbing algorithm to detect factors related to multimorbidity.tif by Wenzhu Song (12532351)

“…In this study, we included 10 variables from data on demographic background, health status and functioning, and lifestyle. …”

Image_1_Using Bayesian networks with Max-Min Hill-Climbing algorithm to detect factors related to multimorbidity.tif by Wenzhu Song (12532351)

“…In this study, we included 10 variables from data on demographic background, health status and functioning, and lifestyle. …”

Image_3_Using Bayesian networks with Max-Min Hill-Climbing algorithm to detect factors related to multimorbidity.tif by Wenzhu Song (12532351)

“…In this study, we included 10 variables from data on demographic background, health status and functioning, and lifestyle. …”

Table_5_Benchmarking freely available HLA typing algorithms across varying genes, coverages and typing resolutions.csv by Nikolas Hallberg Thuesen (14072517)

Table_4_Benchmarking freely available HLA typing algorithms across varying genes, coverages and typing resolutions.csv by Nikolas Hallberg Thuesen (14072517)

Table_2_Benchmarking freely available HLA typing algorithms across varying genes, coverages and typing resolutions.csv by Nikolas Hallberg Thuesen (14072517)

Table_1_Benchmarking freely available HLA typing algorithms across varying genes, coverages and typing resolutions.csv by Nikolas Hallberg Thuesen (14072517)

Table_6_Benchmarking freely available HLA typing algorithms across varying genes, coverages and typing resolutions.csv by Nikolas Hallberg Thuesen (14072517)

Table_3_Benchmarking freely available HLA typing algorithms across varying genes, coverages and typing resolutions.csv by Nikolas Hallberg Thuesen (14072517)

Route for bays29 output by ABSQL algorithm. by Jin Zhang (53297)

“…DSRABSQL builds upon the Q-learning (QL) algorithm. Considering its problems of slow convergence and low accuracy, four strategies within the QL framework are designed first: the weighting function-based reward matrix, the power function-based initial Q-table, a self-adaptive <i>ε-beam</i> search strategy, and a new Q-value update formula. …”

Output groupings of the 3 algorithms for a linear degradation chain of <i>N</i> = 3 metabolites. by Yuanchen Zhao (12905580)

Subjects:

libEnsemble: New capabilities for coordinating dynamic ensembles of calculations by John-Luke Navarro (9024008)

Radius of gyration (RG) of the backbone atoms of the protein as a function of time. by L. F. S. Bonet (11502082)

Solvent-accessible surface area (SASA) of the protein as a function of time. by L. F. S. Bonet (11502082)